To address the low efficiency and lack of precision in existing methods for extracting rivet unevenness features from three-dimensional point clouds, we propose an accurate extraction method based on point cloud dimensionality reduction. First, the surface variant of the point cloud is calculated, and the surface normal variant is color-mapped through mathematical processing. Next, principal component analysis projection technique combined with two-dimensional meshing is used for point cloud dimensionality reduction, generating an image, and the relevant image processing algorithms are used for the coarse segmentation of the point cloud in the rivet region. Finally, hierarchical structure fitting is applied to accurately extract the rivet head and skin point clouds, allowing for the calculation of rivet unevenness. Experimental results show high computational efficiency with large point cloud datasets and a computational error of less than 0.013 mm.